Uni-RNA-L12
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- DP Technology
- Organisation type
- Industry
- Country
- China
- Published
- 12 July 2023
- Authors
- Xi Wang, Ruichu Gu, Zhiyuan Chen, Yongge Li, Xiaohong Ji, Guolin Ke, Han Wen
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 85M
- Training data
- tokens
Table 8: Model architecture parameters of different Uni-RNA models
Table 8: Model architecture parameters of different Uni-RNA models Sequences are capped at 4096 length, but no average sequence length is given. Estimating at 500 tokens per sequence. 100000000*500=
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA A100
- Chips used
- 128
- Power draw
- 101.8 kW
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 20
Sources
Where this record came from and when it was last checked.
- Reference
- Uni-RNA: Universal Pre-Trained Models Revolutionize RNA Research
- Last updated
- 28 November 2025
What the numbers mean
Background
Uni-RNA-L12 was published by DP Technology, in China, in July 2023. It comes out of industry.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Uni-RNA-L12 — common questions
What is Uni-RNA-L12 used for?
Uni-RNA-L12 works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Uni-RNA-L12?
None. Uni-RNA-L12 is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Uni-RNA-L12 open source?
No. Uni-RNA-L12 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Uni-RNA-L12 have?
Uni-RNA-L12 has 85M parameters. Table 8: Model architecture parameters of different Uni-RNA models. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Uni-RNA-L12?
Uni-RNA-L12 was published by DP Technology, based in China, categorised as industry.
When was Uni-RNA-L12 released?
Uni-RNA-L12 was published in July 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.